De invloed van inkomst op het risico van diabetes

De invloed van inkomst op het risico van diabetes#

# Import packages
import pandas as pd
import plotly.express as px
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import plotly.graph_objs as go
from plotly.subplots import make_subplots
from matplotlib.ticker import PercentFormatter
import warnings
warnings.filterwarnings('ignore')

studenten: Pepe Morselt (15239551), Marilène Oud (15247112), Paul Elsinghorst (15002608), Thimo de Wolff (15240428)

groep: M2

link to dataset3: https://www.kaggle.com/datasets/henryshan/sleep-health-and-lifestyle

Introductie#

Diabetes is een chronische ziekte die impact heeft op miljoenen mensen. Het lichaam kan glucose niet goed reguleren, wat kan leiden tot een te hoge bloedsuikerspiegel en, indien niet behandeld, schade aan verschillende organen.

Diabetes komt in twee vormen voor: diabetes type 1 en type 2. In deze datastory richten we ons voornamelijk op type 2, omdat er verschillende correlaties kunnen worden gevonden tussen factoren zoals levensstijl, voeding en gezondheid. Deze factoren beïnvloeden het ontstaan van diabetes type 2, in tegenstelling tot diabetes type 1, dat voornamelijk genetisch bepaald is. Door te onderzoeken naar hoe fysieke activiteit en omgevingsfactoren bijdragen aan deze aandoening bij mensen met verschillende inkomens, kunnen we nagaan of er een hogere of lagere kans is op het ontstaan van diabetes type 2 bij verschillende inkomensgroepen. Met deze datastory hopen we dit probleem te belichten zodat er effectievere preventie- en behandelstrategieën ontwikkeld kunnen worden die de levenskwaliteit verbeteren voor mensen met diabetes die financieel kwetsbaar zijn.

Onze perspectieven en argumenten zijn de volgende:

Het risico op het ontwikkelen van diabetes hangt sterk samen met het inkomen van een persoon, waardoor een individu met een lager inkomen meer kans heeft om diabetes te ontwikkelen.

Arg 1: Mensen met een lager inkomen ervaren vaak een beperkte toegang tot gezonde voeding, tijd voor fysieke activiteit en gezondheidszorg.

Arg 2: Mensen met een lager inkomen wonen in buurten met minder supermarkten die verse, niet bewerkte producten aanbieden en meer fastfoodrestaurants.

Arg 3: Goedkope etenswaren zijn vaak ongezond.

Arg 4: De prevalentie van diabetes type 2 is hoger in landen met een lager BBP per capita.

Het risico op diabets is niet uitsluitend afhankelijk van inkomen, maar van de beslissingen die individuen zelf nemen.

Dataset en Preprocessing#

df = pd.read_csv("diabetes_split.csv")

num_bins = 5
df['BMI_category'] = pd.qcut(df['BMI'], q=num_bins, labels=False, precision=0)
bin_labels = ['Underweight', 'Normal weight', 'Overweight', 'Moderately obese', 'Severely obese']

df['BMI_category'] = df['BMI_category'].map(lambda x: bin_labels[int(x)])


fig_1 = go.Figure(data=
    go.Parcats(
        dimensions=[
            {'label': 'Diabetes', 'values': df['Diabetes_binary'].astype(str)},
            {'label': 'BMI', 'values': df['BMI_category']},  # Use BMI categories here
            {'label': 'Physical Activity', 'values': df['PhysActivity'].astype(str)},
            {'label': 'Vegetables', 'values': df['Veggies'].astype(str)},
            {'label': 'Any Healthcare', 'values': df['AnyHealthcare'].astype(str)},
            {'label': 'General Health', 'values': df['GenHlth'].astype(str)},
            {'label': 'Sex', 'values': df['Sex'].astype(str)}
        ],
        line={'color': df['Diabetes_binary'], 'colorscale': [[0, 'blue'], [1.0, 'yellow']]}
    )
)

fig_1.update_layout(title='Analysis of Diabetes Data across Various Factors')
fig_1.show()

#https://www.kaggle.com/datasets/mathchi/diabetes-data-set
df = pd.read_csv("diabetes.csv")
pd.set_option('display.max_columns', None)
df = df.rename(columns={'Outcome': 'diabetes'})
df.head(5)
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age diabetes
0 6 148 72 35 0 33.6 0.627 50 1
1 1 85 66 29 0 26.6 0.351 31 0
2 8 183 64 0 0 23.3 0.672 32 1
3 1 89 66 23 94 28.1 0.167 21 0
4 0 137 40 35 168 43.1 2.288 33 1 
#https://www.kaggle.com/datasets/iammustafatz/diabetes-prediction-dataset
df2 = pd.read_csv("diabetes_prediction_dataset.csv")
pd.set_option('display.max_columns', None)
df2.head(5)
gender age hypertension heart_disease smoking_history bmi HbA1c_level blood_glucose_level diabetes
0 Female 80.0 0 1 never 25.19 6.6 140 0
1 Female 54.0 0 0 No Info 27.32 6.6 80 0
2 Male 28.0 0 0 never 27.32 5.7 158 0
3 Female 36.0 0 0 current 23.45 5.0 155 0
4 Male 76.0 1 1 current 20.14 4.8 155 0 
df3 = pd.read_csv("ss.csv")
df3['BMI Category'] = df3['BMI Category'].replace('Normal Weight', 'Normal')
df3.head(5)
Person ID Gender Age Occupation Sleep Duration Quality of Sleep Physical Activity Level Stress Level BMI Category Blood Pressure Heart Rate Daily Steps Sleep Disorder
0 1 Male 27 Software Engineer 6.1 6 42 6 Overweight 126/83 77 4200 NaN
1 2 Male 28 Doctor 6.2 6 60 8 Normal 125/80 75 10000 NaN
2 3 Male 28 Doctor 6.2 6 60 8 Normal 125/80 75 10000 NaN
3 4 Male 28 Sales Representative 5.9 4 30 8 Obese 140/90 85 3000 Sleep Apnea
4 5 Male 28 Sales Representative 5.9 4 30 8 Obese 140/90 85 3000 Sleep Apnea

Observaties#

Er bestaat een correlatie tussen obese zijn en een hogere bloeddruk. De Mediaan gaat duidelijk omhoog met elke BMI categorie. De boxpolts zijn wat onduidelijk in de hogere categorïen. Volgens deze statistieken hebben vrouwen over het algemeen een lagere bloeddruk.

df2 = df2[df2['smoking_history'] != 'No Info']
diabetes_smoking_summary = df2.groupby(['diabetes', 'smoking_history']).size().unstack()

fig, axes = plt.subplots(1, 2, figsize=(14, 7))

for i, diabetes_category in enumerate(diabetes_smoking_summary.index):
    axes[i].pie(diabetes_smoking_summary.loc[diabetes_category],
                labels=diabetes_smoking_summary.columns,
                autopct='%1.1f%%',
                startangle=90)
    axes[i].set_title(f'Diabetes: {diabetes_category}')

plt.suptitle('Distribution of Smoking History by Diabetes Status')
plt.show()
../_images/d267969052ac75c8c76395dd572293afa3579ff769d877dfe84de15bcfecf401.png

Observaties#

Deze gegevens zijn gefilterd zodat de gegevens waarvan we hun niet in het cirkeldiagram zijn geplaatst, wat tot een verkeerde visualisatie van de gegevens kan leiden.

Mensen met diabetes hebben vaker gerookt. We hebben echter nog niet ontdekt waarom dit het geval is.

import plotly.express as px
import pandas as pd

diab_prev = pd.read_csv('diabetes-prevalence.csv')

diab_prev['Year'] = pd.to_numeric(diab_prev['Year'])

diab_prev_sorted = diab_prev.sort_values(by='Year')

fig = px.choropleth(diab_prev_sorted, 
                    locations='Code',
                    color='Diabetes prevalence (% of population ages 20 to 79)',  # Column with data to plot
                    hover_name='Entity',
                    animation_frame='Year',
                    color_continuous_scale='YlOrRd',
                    projection='natural earth'
                   )

fig.update_layout(title='Diabetes Prevalence Worldwide',
                  geo=dict(showcoastlines=True,
                           showcountries=True),
                  height=600,
                  width=1000
                 )

fig.show()

df4 = pd.read_csv("weight_20_USA.csv")
df5 = pd.read_csv("Weight_children_USA.csv")
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
df4.head(1)
INDICATOR PANEL PANEL_NUM UNIT UNIT_NUM STUB_NAME STUB_NAME_NUM STUB_LABEL STUB_LABEL_NUM YEAR YEAR_NUM AGE AGE_NUM ESTIMATE SE FLAG
0 Normal weight, overweight, and obesity among a... Normal weight (BMI from 18.5 to 24.9) 1 Percent of population, age-adjusted 1 Total 1 20 years and over 1.1 1988-1994 1 20 years and over 1.0 41.6 0.8 NaN 
df5.head(1)
INDICATOR PANEL PANEL_NUM UNIT UNIT_NUM STUB_NAME STUB_NAME_NUM STUB_LABEL_NUM STUB_LABEL YEAR YEAR_NUM AGE AGE_NUM ESTIMATE SE FLAG
0 Obesity among children and adolescents aged 2-... 2-19 years 1 Percent of population, crude 1 Total 0 0.0 2-19 years 1988-1994 1 2-19 years 0.0 10.0 0.5 NaN 
def print_column_headers(dataset):
    """
    Prints all the column headers of a pandas DataFrame.
    
    Parameters:
    dataset (pd.DataFrame): The dataset from which to print column headers.
    """
    if not isinstance(dataset, pd.DataFrame):
        raise TypeError("The input dataset must be a pandas DataFrame.")
    
    print("Column Headers:")
    for col in dataset.columns:
        print(col)
print_column_headers(df4)

Column Headers:
INDICATOR
PANEL
PANEL_NUM
UNIT
UNIT_NUM
STUB_NAME
STUB_NAME_NUM
STUB_LABEL
STUB_LABEL_NUM
YEAR
YEAR_NUM
AGE
AGE_NUM
ESTIMATE
SE
FLAG

required_columns = ['YEAR', 'PANEL', 'ESTIMATE']
for col in required_columns:
    if col not in df4.columns:
        raise ValueError(f"Missing required column: {col}")

# Group by YEAR and PANEL (poverty level), and calculate the mean estimate (assuming ESTIMATE is the diabetes ratio)
diabetes_ratio = df4.groupby(['YEAR', 'PANEL'])['ESTIMATE'].mean().reset_index()

# Pivot the DataFrame to have years as rows and poverty levels as columns for easier plotting
pivot_table = diabetes_ratio.pivot(index='YEAR', columns='PANEL', values='ESTIMATE')

# Plot the data
plt.figure(figsize=(12, 6))
sns.lineplot(data=pivot_table, markers=True)
plt.title('Ratio of Diabetes per Poverty Level Over the Years')
plt.xlabel('Year')
plt.ylabel('Diabetes Ratio')
plt.legend(title='Poverty Level')
plt.show()
../_images/792352d6bb245348671bc8df1d3aeb858f9829b76d93f950344dbf70c391e95a.png 
filtered_dataset = df4[df4['STUB_NAME'] == 'Total']

filtered_dataset = df5[df5['STUB_NAME'].isin(['Percent of poverty level'])]

filtered_recent = df5[df5['YEAR_NUM'] == 10] 

filtered_recent_genders = filtered_recent[
    (filtered_recent['STUB_LABEL'].isin(['Male', 'Female'])) | 
    (filtered_recent['STUB_NAME'] == 'Percent of poverty level')]

selected_col = ['INDICATOR', 'AGE', 'STUB_LABEL', 'ESTIMATE']
filtered_df = filtered_recent_genders[selected_col]

from matplotlib.ticker import PercentFormatter
filtered_df_2 = filtered_df[filtered_df['AGE'] != '2-19 years']

plt.figure(figsize=(14, 8))
sns.lineplot(data=filtered_df_2, x='AGE', y='ESTIMATE', hue='STUB_LABEL', markers=True, dashes=False)

plt.title('Childhood obesity per gender and household income in the USA')
plt.xlabel('Age')
plt.ylabel('Obesity percentage')
plt.legend(title='Legend for Labels')
plt.grid(True)
plt.gca().yaxis.set_major_formatter(PercentFormatter(xmax=100))
plt.show()
../_images/3928f6a8ac8749e247c45795baccd3efd68c630c2b26272c9cef75a01ad29ba9.png

Perspectief 1#

Mensen met een lager inkomen hebben minder toegang tot gezonde voeding doordat gezonde voeding duurder is.#

try:
    df7 = pd.read_csv("faps_access_puf.csv", encoding='latin-1')
    df8 = pd.read_csv("faps_fahitem_puf.csv", encoding='latin-1')
    df9 = pd.read_csv("faps_fafhitem_puf.csv", encoding='latin-1')
except UnicodeDecodeError as e:
    print(f"UnicodeDecodeError: {e}")
df7.head(2)
hhnum infousa_flag snap1 snap2 snap3 snap4 snap5 snap6 snap7 snap8 ss1 ss2 ss3 ss4 ss5 ss6 ss7 ss8 sm1 sm2 sm3 sm4 sm5 sm6 sm7 sm8 co1 co2 co3 co4 co5 co6 co7 co8 cs1 cs2 cs3 cs4 cs5 cs6 cs7 cs8 mlg1 mlg2 mlg3 mlg4 mlg5 mlg6 mlg7 mlg8 dist_ss dist_sm dist_co dist_cs dist_mlg dist_walmart nearsnap_placeid nearsnap_stype nearsnap_dist nearsmss_placeid nearsmss_stype nearsmss_dist ff1 ff2 ff3 ff4 ff5 ff6 ff7 ff8 nonff1 nonff2 nonff3 nonff4 nonff5 nonff6 nonff7 nonff8 nearff_sic1 nearff_sic2 nearff_dist nearnonff_sic1 nearnonff_sic2 nearnonff_dist nearmcd_sic1 nearmcd_sic2 nearmcd_dist
0 100012 1 0 4 9 16 19 22 37 188 0 0 1 2 2 2 3 13 0 1 1 1 1 1 1 14 0 1 5 5 7 8 14 67 0 0 0 4 5 7 12 62 0 2 2 3 3 3 6 11 0.90 0.33 0.34 1.03 0.33 0.9 1017721.0 SM 0.33 1017721.0 SM 0.33 0 1 10 11 12 12 14 77 0 4 15 21 26 26 36 222 581208 581206 0.30 581208 581208 0.3 581208 581206 0.527480
1 100015 1 1 6 18 43 52 80 118 512 0 0 1 2 2 3 5 21 0 0 0 3 5 6 9 41 0 1 4 12 14 20 27 141 0 3 10 15 17 30 43 182 0 0 0 3 3 7 10 36 0.94 1.05 0.32 0.26 1.16 1.7 NaN ME 0.19 1160329.0 SS 0.94 1 1 2 15 16 22 28 39 2 4 16 35 43 60 75 113 581208 581206 0.21 581208 581208 0.2 581208 581206 1.546103 
df8.head(2)
hhnum eventid itemnum itemdesc itemdescsource itemdesc_flag barcode barcodesource upcreceiptmatch iri barcode_original itemreportmethod itemassignmethod totgramsunadj totgramsunadjimp pkgsize pkgsizeunit fluidozgrams eggsize quantity varwgtlbs varwgtcount pkgwtsource totitemexp totitemexpnocoupons totstoresavings coupons totitemexp_flag free imputedexp imputedvalue imputemethod scandate_flag
0 100012 65792 1 NaN 1 0 051933267503 1 NaN 1 .V 1 2 340.2 NaN 12.0 OZ NaN NaN 1 NaN NaN 1.0 0.89 0.89 0.0 0.0 0 0.0 -996.0 -996.0 -996 0
1 100012 65792 2 NaN 1 0 051933161825 1 NaN 3 .V 1 2 NaN NaN NaN NaN 30.7 NaN 1 NaN NaN NaN 0.79 0.79 0.0 0.0 0 0.0 -996.0 -996.0 -996 0 
df9.head(2)
hhnum eventid itemnum bundlenum bundletype menugrp nonschmealitem itemdesc itemdescsource quantity quantity_flag size size_flag sizeunit sizeunit_flag sizerelative gramstotal additem_flag itemcost itemcost_flag totitemcost free free_flag2 impitemcost impcostmethod imptotcost
0 100012 65766 1 NaN NaN TOP -996.0 Double Cheeseburger 7 1.0 0 NaN 0 NaN 0 NaN 165.0 0 0.0 0 0.0 1 0 NaN -996 NaN
1 100012 65767 1 NaN NaN BEV -996.0 REGULAR SODA, SPRITE, SIERRA MIST, MOUNTAIN DEW 5 3.0 0 12.0 0 OZ 1 NaN 1108.8 0 0.5 0 1.5 0 0 NaN -996 NaN

which macronutriens are unhealty?#

Fats:

Fats consist of 3 main types; saturated fats, which can usually be found in dairy products, monounsaturated fats which can be found in nuts, seeds and oils, and polyunsaturated fats, which can be found in falxseeds, walnuts and fish.

Since saturated fats take less time to be stored or processed due to their relative simplicity and their tendency to raise LDL cholesterol (low-density lipoprotein), which can increase the risk of cardiovascular disease by clogging the arteries, they are generally regarded as the worst kind of fat.

Less than 5-6% of daily calorie intake should be saturated fat which is about 11-13 grams a day if you account for a 2000 kcal diet, so about 120 kcal. https://www.heart.org/en/healthy-living/healthy-eating/eat-smart/fats/saturated-fats

Carbohydrates:

Carbs consist of 3 main types; Sugars, Starches and Fibers.

Sugars are incomplex carbohydrates such as mono- and disaccharides which can be easily converted into glucose (which is a monosaccharid) and can then either be converted into fat for storage or be consumed for energy, an acces causes insulin to be released in the pancreas and convert these monosaccharides into fats.

Starches are longer chains of saccharides, they can be found in starchy foods such as beans and potatoes, these polysaccharides take longer to be converted into glucose so they provide sugars in a slower tempo and keep you filled for quite some time. So they are regarded as a healthy carbs.

Fibers promote gut health and are not digested because they are too complex. animals with multiple stomachs such as cows do eat these as a source of carbs.

Generally people shouldn’t consume more than 30 grams or about 125 kcal of monosaccharides per day.

https://www.heart.org/en/healthy-living/healthy-eating/eat-smart/sugar/how-much-sugar-is-too-much https://www.ncbi.nlm.nih.gov/books/NBK459280/

We will use these macronutriens to measure if a certain food is healthy or not. If a fooditem has more than 1/2 of the daily recommended intake of either sugars or saturated fat, or if the total amount of carbs per fooditem consists of more than 11.5% sugar it will be regarded as unhealthy. Fatty food will be regarded unhealthy if more than 20% of the total fat is saturated fat. variables: ‘hhnum’, ‘itemnum’, ‘usdadescmain’,

df10 = pd.read_csv("faps_fahnutrients.csv")
df10.head(50)
hhnum eventid itemnum foodcode foodcodetype codenot1112 foodcodefped foodcodeassignmethod usdadescmain usdadescadd usdafoodcat1 usdafoodcat2 usdafoodcat4 recodedfinal foodgroup foodcodempr foodcodempr_flag totgramsunadj totgramsedible totgramsunadjimp totgramsedibleimp dryweightcalc fluidozgrams eggsize refuse refuseedit refusempr refusesourcempr d_total d_cheese d_milk d_yogurt f_total f_citmlb f_other f_juice g_total g_refined g_whole pf_total pf_mps_total pf_meat pf_poult pf_seafd_hi pf_seafd_low pf_organ pf_curedmeat pf_eggs pf_soy pf_nutsds pf_legumes v_legumes v_total v_drkgr v_redor_total v_redor_tomato v_redor_other v_starchy_total v_starchy_potato v_starchy_other v_other oils solid_fats add_sugars a_drinks energy carb dietfiber totsug totfat satfat monofat polyfat protein alcohol caroalpha carobeta chol caffeine calcium choline copper crypt folacid foldfe folfood foltot iron lutein lycopene magnes niacin phosp potass retinol ribofl selenium sodium theobrom thiamin vitarae vitb6 vitb12 vitb12add vitc vitd vite viteadd vitk water zinc sfa40 sfa60 sfa80 sfa100 sfa120 sfa140 sfa160 sfa180 mfa161 mfa181 mfa201 mfa221 pfa182 pfa183 pfa184 pfa204 pfa205 pfa225 pfa226
0 100381 5897 1 11112110.0 1 0.0 NaN 3 Milk, cow's, fluid, 2% fat Hi-Protein milk; fortified milk; 1.5% fat milk... 1.0 10.0 1004.0 0 40201 NaN 0.0 3904.0000 3904.000000 NaN NaN 0 30.5 NaN NaN 0 NaN NaN 0.41 0.00 0.41 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 1.37 0.00 0.0 50.0 4.80 0.0 5.06 1.98 1.257 0.560 0.073 3.30 0.0 0.0 4.0 8.0 0.0 120.0 16.4 0.006 0.0 0.0 5.0 5.0 5.0 0.02 0.0 0.0 11.0 0.092 92.0 140.0 55.0 0.185 2.5 47.0 0.0 0.039 55.0 0.038 0.53 0.0 0.2 1.2 0.03 0.00 0.2 89.21 0.48 0.077 0.040 0.020 0.049 0.055 0.175 0.558 0.243 0.027 0.508 0.002 0.000 0.062 0.008 0.000 0.000 0.000 0.000 0.000
1 100381 5897 2 11112110.0 1 0.0 NaN 3 Milk, cow's, fluid, 2% fat Hi-Protein milk; fortified milk; 1.5% fat milk... 1.0 10.0 1004.0 0 40201 NaN 0.0 3904.0000 3904.000000 NaN NaN 0 30.5 NaN NaN 0 NaN NaN 0.41 0.00 0.41 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 1.37 0.00 0.0 50.0 4.80 0.0 5.06 1.98 1.257 0.560 0.073 3.30 0.0 0.0 4.0 8.0 0.0 120.0 16.4 0.006 0.0 0.0 5.0 5.0 5.0 0.02 0.0 0.0 11.0 0.092 92.0 140.0 55.0 0.185 2.5 47.0 0.0 0.039 55.0 0.038 0.53 0.0 0.2 1.2 0.03 0.00 0.2 89.21 0.48 0.077 0.040 0.020 0.049 0.055 0.175 0.558 0.243 0.027 0.508 0.002 0.000 0.062 0.008 0.000 0.000 0.000 0.000 0.000
2 100381 5897 3 75102000.0 1 0.0 NaN 6 Beans, lima, raw NaN 6.0 64.0 6418.0 1 20102 11037.0 0.0 340.2000 340.200012 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.65 0.00 0.00 0.0 0.00 0.65 0.0 0.65 0.00 0.00 0.00 0.00 0.0 113.0 20.17 4.9 1.48 0.86 0.198 0.050 0.419 6.84 0.0 0.0 126.0 0.0 0.0 34.0 40.0 0.318 0.0 0.0 34.0 34.0 34.0 3.14 0.0 0.0 58.0 1.474 136.0 467.0 0.0 0.103 1.8 8.0 0.0 0.217 10.0 0.204 0.00 0.0 23.4 0.0 0.32 0.00 5.6 70.24 0.78 0.000 0.000 0.000 0.000 0.000 0.002 0.173 0.022 0.000 0.050 0.000 0.000 0.283 0.136 0.000 0.000 0.000 0.000 0.000
3 100381 5897 4 75102000.0 1 0.0 NaN 6 Beans, lima, raw NaN 6.0 64.0 6418.0 1 20102 11037.0 0.0 340.2000 340.200012 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.65 0.00 0.00 0.0 0.00 0.65 0.0 0.65 0.00 0.00 0.00 0.00 0.0 113.0 20.17 4.9 1.48 0.86 0.198 0.050 0.419 6.84 0.0 0.0 126.0 0.0 0.0 34.0 40.0 0.318 0.0 0.0 34.0 34.0 34.0 3.14 0.0 0.0 58.0 1.474 136.0 467.0 0.0 0.103 1.8 8.0 0.0 0.217 10.0 0.204 0.00 0.0 23.4 0.0 0.32 0.00 5.6 70.24 0.78 0.000 0.000 0.000 0.000 0.000 0.002 0.173 0.022 0.000 0.050 0.000 0.000 0.283 0.136 0.000 0.000 0.000 0.000 0.000
4 100381 5897 5 75102000.0 1 0.0 NaN 6 Beans, lima, raw NaN 6.0 64.0 6418.0 1 20102 11037.0 0.0 340.2000 340.200012 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.65 0.00 0.00 0.0 0.00 0.65 0.0 0.65 0.00 0.00 0.00 0.00 0.0 113.0 20.17 4.9 1.48 0.86 0.198 0.050 0.419 6.84 0.0 0.0 126.0 0.0 0.0 34.0 40.0 0.318 0.0 0.0 34.0 34.0 34.0 3.14 0.0 0.0 58.0 1.474 136.0 467.0 0.0 0.103 1.8 8.0 0.0 0.217 10.0 0.204 0.00 0.0 23.4 0.0 0.32 0.00 5.6 70.24 0.78 0.000 0.000 0.000 0.000 0.000 0.002 0.173 0.022 0.000 0.050 0.000 0.000 0.283 0.136 0.000 0.000 0.000 0.000 0.000
5 100381 5897 6 63107010.0 1 0.0 NaN 3 Banana, raw NaN 6.0 60.0 6004.0 0 30101 NaN 0.0 598.7388 383.192841 NaN NaN 1 NaN NaN 36.0 0 36.0 2.0 0.00 0.00 0.00 0.00 0.67 0.00 0.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.00 0.00 0.0 89.0 22.84 2.6 12.23 0.33 0.112 0.032 0.073 1.09 0.0 25.0 26.0 0.0 0.0 5.0 9.8 0.078 0.0 0.0 20.0 20.0 20.0 0.26 22.0 0.0 27.0 0.665 22.0 358.0 0.0 0.073 1.0 1.0 0.0 0.031 3.0 0.367 0.00 0.0 8.7 0.0 0.10 0.00 0.5 74.91 0.15 0.000 0.000 0.000 0.001 0.002 0.002 0.102 0.005 0.010 0.022 0.000 0.000 0.046 0.027 0.000 0.000 0.000 0.000 0.000
6 100381 5897 7 11507.0 3 0.0 NaN 3 Sweet potato, raw, unprepared NaN 6.0 64.0 6406.0 0 20401 NaN 0.0 NaN NaN 650.0 468.0 1 NaN NaN 0.0 1 28.0 2.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.77 0.00 0.77 0.0 0.77 0.00 0.0 0.00 0.00 0.00 0.00 0.00 0.0 86.0 20.12 3.0 4.18 0.05 0.018 0.001 0.014 1.57 0.0 7.0 8509.0 0.0 0.0 30.0 12.3 0.151 0.0 0.0 11.0 11.0 11.0 0.61 0.0 0.0 25.0 0.557 47.0 337.0 0.0 0.061 0.6 55.0 0.0 0.078 709.0 0.209 0.00 0.0 2.4 0.0 0.26 0.00 1.8 77.28 0.30 0.000 0.000 0.000 0.000 0.000 0.000 0.018 0.001 0.000 0.001 0.000 0.000 0.013 0.001 0.000 0.000 0.000 0.000 0.000
7 100381 5897 8 63420110.0 1 0.0 NaN 6 Fruit juice bar, frozen, flavor other than orange Dole Fruit 'N Juice Bar; Frozfruit Bar; Popsic... 5.0 58.0 5806.0 1 70407 13120100.0 0.0 NaN NaN NaN NaN 0 29.8 NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.12 0.00 0.02 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.00 2.85 0.0 87.0 20.20 1.0 17.48 0.10 0.000 0.000 0.030 1.20 0.0 0.0 11.0 0.0 0.0 5.0 4.0 0.000 0.0 0.0 7.0 7.0 7.0 0.19 34.0 0.0 4.0 0.159 6.0 53.0 0.0 0.018 0.2 4.0 0.0 0.011 1.0 0.025 0.00 0.0 9.5 0.0 0.00 0.00 0.9 78.30 0.05 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.022 0.008 0.000 0.000 0.000 0.000 0.000
8 100381 5897 9 75109600.0 1 0.0 NaN 3 Corn, raw NaN 6.0 64.0 6416.0 0 20101 NaN 0.0 NaN NaN 286.0 103.0 1 NaN NaN 64.0 0 64.0 2.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.67 0.00 0.00 0.0 0.00 0.67 0.0 0.67 0.00 0.00 0.00 0.00 0.0 86.0 18.70 2.0 6.26 1.35 0.325 0.432 0.487 3.27 0.0 16.0 47.0 0.0 0.0 2.0 23.0 0.054 115.0 0.0 42.0 42.0 42.0 0.52 644.0 0.0 37.0 1.770 89.0 270.0 0.0 0.055 0.6 15.0 0.0 0.155 9.0 0.093 0.00 0.0 6.8 0.0 0.07 0.00 0.3 76.05 0.46 0.000 0.000 0.000 0.003 0.001 0.001 0.262 0.045 0.004 0.419 0.006 0.000 0.472 0.014 0.000 0.001 0.000 0.000 0.000
9 100381 5897 10 NaN 0 NaN NaN 0 NaN NaN NaN NaN NaN 0 99999 NaN 0.0 NaN NaN NaN NaN 1 NaN NaN NaN 0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
10 100381 5897 11 63223020.0 1 0.0 NaN 3 Strawberries, raw strawberries, NS as to raw, cooked, canned, or... 6.0 60.0 6010.0 0 30101 NaN 0.0 340.2000 319.787994 NaN NaN 1 NaN NaN 6.0 0 6.0 2.0 0.00 0.00 0.00 0.00 0.69 0.69 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.00 0.00 0.0 32.0 7.68 2.0 4.89 0.30 0.015 0.043 0.155 0.67 0.0 0.0 7.0 0.0 0.0 16.0 5.7 0.048 0.0 0.0 24.0 24.0 24.0 0.41 26.0 0.0 13.0 0.386 24.0 153.0 0.0 0.022 0.4 1.0 0.0 0.024 1.0 0.047 0.00 0.0 58.8 0.0 0.29 0.00 2.2 90.95 0.14 0.000 0.000 0.000 0.000 0.000 0.000 0.012 0.003 0.001 0.042 0.000 0.000 0.090 0.065 0.000 0.000 0.000 0.000 0.000
11 100381 5897 12 23625.0 3 0.0 NaN 3 Beef, top sirloin, steak, separable lean only,... NaN 2.0 20.0 2002.0 0 50101 NaN 0.0 NaN NaN NaN NaN 1 NaN NaN 17.0 0 17.0 2.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.65 2.65 2.65 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.00 0.00 0.0 135.0 0.00 0.0 0.00 4.62 1.709 1.860 0.214 21.91 0.0 0.0 0.0 61.0 0.0 28.0 91.5 0.073 0.0 0.0 13.0 13.0 13.0 1.63 0.0 0.0 23.0 7.420 205.0 342.0 0.0 0.107 25.5 57.0 0.0 0.061 0.0 0.601 1.19 0.0 0.0 0.1 0.33 0.00 1.3 72.51 4.14 0.000 0.000 0.000 0.000 0.000 0.117 1.026 0.566 0.143 1.715 0.003 0.000 0.161 0.010 0.000 0.029 0.002 0.011 0.001
12 100381 5897 13 75117020.0 1 0.0 NaN 3 Onions, mature, raw red onions; onions, NFS 6.0 64.0 6414.0 0 20601 NaN 0.0 961.6108 865.449707 NaN NaN 1 NaN NaN 10.0 0 10.0 2.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.63 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.63 0.00 0.00 0.00 0.0 40.0 9.34 1.7 4.24 0.10 0.042 0.013 0.017 1.10 0.0 0.0 1.0 0.0 0.0 23.0 6.1 0.039 0.0 0.0 19.0 19.0 19.0 0.21 4.0 0.0 10.0 0.116 29.0 146.0 0.0 0.027 0.5 4.0 0.0 0.046 0.0 0.120 0.00 0.0 7.4 0.0 0.02 0.00 0.4 89.11 0.17 0.000 0.000 0.000 0.000 0.000 0.004 0.034 0.004 0.000 0.013 0.000 0.000 0.013 0.004 0.000 0.000 0.000 0.000 0.000
13 100381 5897 14 63107010.0 1 0.0 NaN 3 Banana, raw NaN 6.0 60.0 6004.0 0 30101 NaN 0.0 598.7388 383.192841 NaN NaN 1 NaN NaN 36.0 0 36.0 2.0 0.00 0.00 0.00 0.00 0.67 0.00 0.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.00 0.00 0.0 89.0 22.84 2.6 12.23 0.33 0.112 0.032 0.073 1.09 0.0 25.0 26.0 0.0 0.0 5.0 9.8 0.078 0.0 0.0 20.0 20.0 20.0 0.26 22.0 0.0 27.0 0.665 22.0 358.0 0.0 0.073 1.0 1.0 0.0 0.031 3.0 0.367 0.00 0.0 8.7 0.0 0.10 0.00 0.5 74.91 0.15 0.000 0.000 0.000 0.001 0.002 0.002 0.102 0.005 0.010 0.022 0.000 0.000 0.046 0.027 0.000 0.000 0.000 0.000 0.000
14 100381 5898 1 91705300.0 1 0.0 NaN 1 Chocolate, sweet or dark Special Dark; Dove Dark Chocolate bar; Hershey... 5.0 57.0 5702.0 0 70403 NaN 0.0 124.7400 124.739998 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 33.20 11.62 0.0 528.0 60.18 6.4 48.81 33.20 19.536 10.510 1.048 4.50 0.0 4.0 12.0 4.0 56.0 40.0 28.8 0.825 0.0 0.0 2.0 2.0 2.0 5.44 24.0 0.0 132.0 0.708 180.0 429.0 0.0 0.146 3.2 20.0 470.0 0.023 1.0 0.042 0.12 0.0 0.0 0.0 0.40 0.00 7.2 0.75 1.80 0.027 0.021 0.014 0.041 0.037 0.138 8.258 10.741 0.092 10.402 0.009 0.000 0.988 0.056 0.000 0.001 0.000 0.000 0.000
15 100381 5898 2 75117020.0 1 0.0 NaN 3 Onions, mature, raw red onions; onions, NFS 6.0 64.0 6414.0 0 20601 NaN 0.0 NaN NaN 300.0 270.0 1 NaN NaN 10.0 0 10.0 2.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.63 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.63 0.00 0.00 0.00 0.0 40.0 9.34 1.7 4.24 0.10 0.042 0.013 0.017 1.10 0.0 0.0 1.0 0.0 0.0 23.0 6.1 0.039 0.0 0.0 19.0 19.0 19.0 0.21 4.0 0.0 10.0 0.116 29.0 146.0 0.0 0.027 0.5 4.0 0.0 0.046 0.0 0.120 0.00 0.0 7.4 0.0 0.02 0.00 0.4 89.11 0.17 0.000 0.000 0.000 0.000 0.000 0.004 0.034 0.004 0.000 0.013 0.000 0.000 0.013 0.004 0.000 0.000 0.000 0.000 0.000
16 100381 5898 3 75117020.0 1 0.0 NaN 3 Onions, mature, raw red onions; onions, NFS 6.0 64.0 6414.0 0 20601 NaN 0.0 NaN NaN 300.0 270.0 1 NaN NaN 10.0 0 10.0 2.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.63 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.63 0.00 0.00 0.00 0.0 40.0 9.34 1.7 4.24 0.10 0.042 0.013 0.017 1.10 0.0 0.0 1.0 0.0 0.0 23.0 6.1 0.039 0.0 0.0 19.0 19.0 19.0 0.21 4.0 0.0 10.0 0.116 29.0 146.0 0.0 0.027 0.5 4.0 0.0 0.046 0.0 0.120 0.00 0.0 7.4 0.0 0.02 0.00 0.4 89.11 0.17 0.000 0.000 0.000 0.000 0.000 0.004 0.034 0.004 0.000 0.013 0.000 0.000 0.013 0.004 0.000 0.000 0.000 0.000 0.000
17 110265 5903 1 55301050.0 1 0.0 NaN 2 French toast sticks, plain NaN 4.0 44.0 4404.0 0 60201 NaN 0.0 1474.2000 1474.199951 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.88 1.88 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 17.43 0.00 2.04 0.0 340.0 41.21 1.4 9.89 17.74 3.989 9.502 2.566 6.00 0.0 0.0 3.0 0.0 0.0 53.0 13.9 0.085 0.0 41.0 212.0 142.0 183.0 1.92 4.0 0.0 19.0 2.575 87.0 111.0 0.0 0.175 12.2 400.0 0.0 0.284 0.0 0.053 0.00 0.0 0.0 0.0 0.88 0.00 14.5 33.57 0.51 0.000 0.000 0.000 0.000 0.000 0.007 1.961 1.892 0.031 9.406 0.065 0.000 2.421 0.145 0.000 0.000 0.000 0.000 0.000
18 110265 5903 2 54401080.0 1 0.0 NaN 1 Salty snacks, corn or cornmeal base, tortilla ... flavored or barbecue-flavored; Tostitos; Dorit... 5.0 50.0 5004.0 0 70502 NaN 0.0 1360.8000 1360.800049 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.94 4.94 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 20.00 0.00 0.00 0.0 473.0 67.52 5.7 0.80 20.84 3.127 6.208 9.838 7.04 0.0 0.0 1.0 0.0 0.0 104.0 19.4 0.094 1.0 0.0 12.0 12.0 12.0 1.56 4.0 0.0 81.0 0.891 215.0 180.0 0.0 0.071 5.2 325.0 0.0 0.146 0.0 0.203 0.36 0.0 0.0 0.0 3.97 0.00 20.9 2.80 1.36 0.000 0.000 0.009 0.011 0.004 0.035 2.504 0.403 0.035 6.077 0.085 0.002 9.473 0.345 0.005 0.005 0.002 0.000 0.000
19 110265 5903 3 41435000.0 1 1.0 NaN 1 Fiber One Fulfill Bar all flavors 5.0 54.0 5404.0 0 70404 NaN 0.0 793.8000 793.799988 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.80 0.12 1.68 2.40 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 2.4 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 7.73 1.65 5.16 0.0 425.0 55.67 17.8 26.67 15.56 1.836 6.852 4.493 15.56 0.0 0.0 0.0 0.0 0.0 44.0 32.7 0.461 0.0 333.0 567.0 0.0 333.0 4.00 46.0 0.0 178.0 8.889 222.0 403.0 499.0 0.756 23.3 378.0 0.0 0.333 499.0 0.667 2.00 0.0 13.3 0.0 2.55 0.00 7.1 11.18 3.33 0.000 0.000 0.003 0.000 0.000 0.008 1.514 0.310 0.005 6.663 0.183 0.000 4.477 0.015 0.000 0.000 0.000 0.000 0.000
20 110265 5903 4 92305000.0 1 0.0 NaN 6 Tea, made from powdered instant, presweetened,... NaN 7.0 73.0 7304.0 1 70301 92307000.0 0.0 2103.5700 2103.570068 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.00 1.68 0.0 30.0 7.31 0.1 7.07 0.05 0.007 0.002 0.016 0.01 0.0 0.0 0.0 0.0 3.0 3.0 0.1 0.010 0.0 0.0 0.0 0.0 0.0 0.02 0.0 0.0 1.0 0.009 0.0 13.0 0.0 0.000 0.0 4.0 0.0 0.001 0.0 0.001 0.00 0.0 0.0 0.0 0.00 0.00 0.0 92.50 0.01 0.000 0.000 0.000 0.000 0.000 0.000 0.006 0.000 0.000 0.002 0.000 0.000 0.011 0.005 0.000 0.000 0.000 0.000 0.000
21 110265 5903 5 92530610.0 1 0.0 NaN 3 Fruit juice drink, with high vitamin C Hi-C; Hawaiian Punch; Ssips; Apple & Eve juice... 7.0 72.0 7204.0 0 70304 NaN 0.0 3955.2000 3955.199951 NaN NaN 0 30.9 NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.06 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.00 2.07 0.0 46.0 11.35 0.1 10.69 0.11 0.017 0.002 0.030 0.13 0.0 0.0 0.0 0.0 0.0 3.0 1.8 0.013 0.0 0.0 0.0 0.0 0.0 0.25 16.0 0.0 5.0 0.036 7.0 122.0 5.0 0.014 0.1 8.0 0.0 0.003 5.0 0.032 0.00 0.0 25.0 0.0 0.05 0.04 0.0 88.10 0.04 0.000 0.000 0.000 0.000 0.000 0.000 0.015 0.002 0.000 0.002 0.000 0.000 0.026 0.004 0.000 0.000 0.000 0.000 0.000
22 110265 5903 6 11830160.0 1 0.0 NaN 2 Cocoa (or chocolate) flavored beverage powder ... Hershey's Instant; Nestle's Quik 1.0 14.0 1402.0 0 70401 NaN 0.0 1380.6450 1380.645020 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 2.68 19.73 0.0 402.0 90.59 4.6 82.85 2.68 2.054 0.504 0.046 3.92 0.0 0.0 0.0 0.0 22.0 246.0 9.4 0.807 0.0 0.0 4.0 4.0 4.0 1.57 3.0 0.0 76.0 0.355 107.0 436.0 0.0 0.101 2.2 151.0 350.0 0.020 0.0 0.460 0.00 0.0 14.0 0.0 0.20 0.00 4.5 1.00 4.18 0.000 0.000 0.000 0.000 0.000 0.007 1.060 0.966 0.007 0.498 0.000 0.000 0.040 0.005 0.000 0.000 0.000 0.000 0.000
23 110265 5903 7 75503040.0 1 0.0 NaN 1 Cucumber pickles, sweet candied dill spears; semi-sweet; gherkin; brea... 8.0 84.0 8408.0 0 20603 NaN 0.0 1814.4000 1814.400024 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.54 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.54 0.00 0.00 4.07 0.0 91.0 21.15 1.0 18.27 0.41 0.067 0.004 0.106 0.58 0.0 81.0 325.0 0.0 0.0 61.0 3.1 0.028 186.0 0.0 1.0 1.0 1.0 0.25 170.0 0.0 7.0 0.115 18.0 100.0 0.0 0.030 0.0 457.0 0.0 0.025 38.0 0.024 0.00 0.0 0.7 0.0 0.36 0.00 47.1 76.20 0.12 0.000 0.000 0.000 0.000 0.002 0.002 0.056 0.006 0.000 0.004 0.000 0.000 0.046 0.060 0.000 0.000 0.000 0.000 0.000
24 110265 5903 8 75114000.0 1 0.0 NaN 6 Mixed salad greens, raw NaN 6.0 64.0 6410.0 1 60101 75143000.0 0.0 1360.8000 1360.800049 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.16 0.98 0.00 0.0 0.00 0.00 0.0 0.00 0.18 0.00 0.00 0.00 0.0 17.0 3.22 2.0 0.92 0.24 0.038 0.008 0.114 1.52 0.0 1.0 3379.0 0.0 0.0 48.0 13.3 0.066 0.0 0.0 108.0 108.0 108.0 1.16 3303.0 0.0 26.0 0.387 31.0 291.0 0.0 0.087 0.5 29.0 0.0 0.068 282.0 0.084 0.00 0.0 10.1 0.0 0.60 0.00 193.4 94.08 0.38 0.000 0.000 0.000 0.000 0.000 0.003 0.032 0.003 0.002 0.005 0.000 0.000 0.039 0.075 0.000 0.000 0.000 0.000 0.000
25 110265 5903 9 31101010.0 1 0.0 NaN 1 Egg, whole, raw NaN 2.0 25.0 2502.0 0 50601 NaN 0.0 1800.0000 1584.000000 NaN NaN 1 NaN 50.0 12.0 0 12.0 2.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 4.25 0.00 0.0 143.0 0.72 0.0 0.37 9.51 3.126 3.658 1.911 12.56 0.0 0.0 0.0 372.0 0.0 56.0 293.8 0.072 9.0 0.0 47.0 47.0 47.0 1.75 503.0 0.0 12.0 0.075 198.0 138.0 160.0 0.457 30.7 142.0 0.0 0.040 160.0 0.170 0.89 0.0 0.0 2.0 1.05 0.00 0.3 76.15 1.29 0.004 0.000 0.004 0.006 0.000 0.033 2.231 0.811 0.201 3.411 0.027 0.000 1.555 0.048 0.000 0.188 0.000 0.007 0.058
26 110265 5903 10 41435000.0 1 1.0 NaN 1 Fiber One Fulfill Bar all flavors 5.0 54.0 5404.0 0 70404 NaN 0.0 793.8000 793.799988 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.80 0.12 1.68 2.40 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 2.4 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 7.73 1.65 5.16 0.0 425.0 55.67 17.8 26.67 15.56 1.836 6.852 4.493 15.56 0.0 0.0 0.0 0.0 0.0 44.0 32.7 0.461 0.0 333.0 567.0 0.0 333.0 4.00 46.0 0.0 178.0 8.889 222.0 403.0 499.0 0.756 23.3 378.0 0.0 0.333 499.0 0.667 2.00 0.0 13.3 0.0 2.55 0.00 7.1 11.18 3.33 0.000 0.000 0.003 0.000 0.000 0.008 1.514 0.310 0.005 6.663 0.183 0.000 4.477 0.015 0.000 0.000 0.000 0.000 0.000
27 110265 5903 11 92512110.0 1 0.0 NaN 3 Margarita mix, nonalcoholic Lemix 7.0 72.0 7204.0 0 70304 NaN 0.0 1977.6000 1977.599976 NaN NaN 0 30.9 NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.00 5.10 0.0 87.0 21.40 0.0 21.40 0.10 0.008 0.002 0.020 0.10 0.0 0.0 0.0 0.0 0.0 2.0 0.9 0.000 0.0 0.0 0.0 0.0 0.0 0.11 0.0 0.0 1.0 0.000 6.0 28.0 0.0 0.010 0.4 102.0 0.0 0.013 0.0 0.000 0.00 0.0 2.7 0.0 0.00 0.00 0.0 78.20 0.07 0.000 0.000 0.000 0.000 0.000 0.000 0.008 0.000 0.000 0.002 0.000 0.000 0.013 0.005 0.000 0.000 0.000 0.000 0.000
28 110265 5903 12 51108010.0 1 0.0 NaN 2 Focaccia, Italian flatbread, plain Parmalat Focaccia; Boboli pizza crust; focacci... 4.0 42.0 4202.0 0 10201 NaN 0.0 368.5500 368.549988 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.06 3.06 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 7.50 0.00 0.26 0.0 249.0 35.82 1.8 1.75 7.89 0.877 5.670 0.994 8.77 0.0 0.0 1.0 0.0 0.0 35.0 2.9 0.091 0.0 54.0 175.0 84.0 138.0 3.16 42.0 0.0 20.0 3.670 128.0 114.0 0.0 0.301 15.9 561.0 0.0 0.470 0.0 0.113 0.00 0.0 0.0 0.0 1.42 0.00 5.7 45.91 1.33 0.000 0.000 0.000 0.000 0.001 0.001 0.723 0.120 0.138 5.495 0.024 0.000 0.923 0.072 0.000 0.000 0.000 0.000 0.000
29 110265 5903 13 63101000.0 1 0.0 NaN 3 Apple, raw NaN 6.0 60.0 6002.0 0 30101 NaN 0.0 223.0000 200.699997 NaN NaN 1 NaN NaN 10.0 0 10.0 2.0 0.00 0.00 0.00 0.00 0.91 0.00 0.91 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.00 0.00 0.0 52.0 13.81 2.4 10.39 0.17 0.028 0.007 0.051 0.26 0.0 0.0 27.0 0.0 0.0 6.0 3.4 0.027 11.0 0.0 3.0 3.0 3.0 0.12 29.0 0.0 5.0 0.091 11.0 107.0 0.0 0.026 0.0 1.0 0.0 0.017 3.0 0.041 0.00 0.0 4.6 0.0 0.18 0.00 2.2 85.56 0.04 0.000 0.000 0.000 0.000 0.000 0.001 0.024 0.003 0.000 0.007 0.000 0.000 0.043 0.009 0.000 0.000 0.000 0.000 0.000
30 110265 5903 14 63101000.0 1 0.0 NaN 3 Apple, raw NaN 6.0 60.0 6002.0 0 30101 NaN 0.0 223.0000 200.699997 NaN NaN 1 NaN NaN 10.0 0 10.0 2.0 0.00 0.00 0.00 0.00 0.91 0.00 0.91 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.00 0.00 0.0 52.0 13.81 2.4 10.39 0.17 0.028 0.007 0.051 0.26 0.0 0.0 27.0 0.0 0.0 6.0 3.4 0.027 11.0 0.0 3.0 3.0 3.0 0.12 29.0 0.0 5.0 0.091 11.0 107.0 0.0 0.026 0.0 1.0 0.0 0.017 3.0 0.041 0.00 0.0 4.6 0.0 0.18 0.00 2.2 85.56 0.04 0.000 0.000 0.000 0.000 0.000 0.001 0.024 0.003 0.000 0.007 0.000 0.000 0.043 0.009 0.000 0.000 0.000 0.000 0.000
31 110265 5903 15 20099.0 3 0.0 NaN 2 Macaroni, dry, enriched NaN 4.0 40.0 4004.0 0 10202 NaN 0.0 2721.6000 2721.600098 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.53 3.53 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.00 0.00 0.0 371.0 74.67 3.2 2.67 1.51 0.277 0.171 0.564 13.04 0.0 0.0 0.0 0.0 0.0 21.0 15.1 0.289 0.0 219.0 391.0 18.0 237.0 3.30 18.0 0.0 53.0 7.177 189.0 223.0 0.0 0.400 63.2 6.0 0.0 0.891 0.0 0.142 0.00 0.0 0.0 0.0 0.11 0.00 0.1 9.90 1.41 0.000 0.000 0.000 0.000 0.000 0.000 0.251 0.025 0.000 0.171 0.000 0.000 0.540 0.024 0.000 0.000 0.000 0.000 0.000
32 103790 5907 1 91102010.0 1 0.0 NaN 3 Sugar, brown NaN 8.0 88.0 8802.0 0 70401 NaN 0.0 907.1800 907.179993 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.00 23.10 0.0 380.0 98.09 0.0 97.02 0.00 0.000 0.000 0.000 0.12 0.0 0.0 0.0 0.0 0.0 83.0 2.3 0.047 0.0 0.0 1.0 1.0 1.0 0.71 0.0 0.0 9.0 0.110 4.0 133.0 0.0 0.000 1.2 28.0 0.0 0.000 0.0 0.041 0.00 0.0 0.0 0.0 0.00 0.00 0.0 1.34 0.03 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
33 103790 5907 2 92530510.0 1 0.0 NaN 4 Cranberry juice drink or cocktail, with high v... cranberry juice, NFS; Ocean Spray cranberry co... 7.0 72.0 7204.0 0 70304 NaN 0.0 NaN NaN NaN NaN 0 31.6 NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.06 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.00 2.35 0.0 54.0 13.52 0.0 11.87 0.10 0.009 0.019 0.059 0.00 0.0 0.0 5.0 0.0 0.0 3.0 1.1 0.010 0.0 0.0 0.0 0.0 0.0 0.10 13.0 0.0 1.0 0.041 1.0 14.0 0.0 0.000 0.2 2.0 0.0 0.000 0.0 0.000 0.00 0.0 42.3 0.0 0.22 0.00 1.0 86.17 0.03 0.000 0.000 0.000 0.000 0.000 0.000 0.007 0.002 0.001 0.019 0.000 0.000 0.035 0.023 0.000 0.000 0.000 0.000 0.000
34 103790 5907 3 82104000.0 1 0.0 NaN 1 Olive oil NaN 8.0 80.0 8012.0 0 70101 NaN 0.0 459.0000 459.000000 NaN NaN 0 27.0 NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 100.00 0.00 0.00 0.0 884.0 0.00 0.0 0.00 100.00 13.808 72.961 10.523 0.00 0.0 0.0 0.0 0.0 0.0 1.0 0.3 0.000 0.0 0.0 0.0 0.0 0.0 0.56 0.0 0.0 0.0 0.000 0.0 1.0 0.0 0.000 0.0 2.0 0.0 0.000 0.0 0.000 0.00 0.0 0.0 0.0 14.35 0.00 60.2 0.00 0.00 0.000 0.000 0.000 0.000 0.000 0.000 11.290 1.953 1.255 71.269 0.311 0.000 9.762 0.761 0.000 0.000 0.000 0.000 0.000
35 103790 5907 4 31101010.0 1 0.0 NaN 1 Egg, whole, raw NaN 2.0 25.0 2502.0 0 50601 NaN 0.0 900.0000 792.000000 NaN NaN 1 NaN 50.0 12.0 0 12.0 2.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 4.25 0.00 0.0 143.0 0.72 0.0 0.37 9.51 3.126 3.658 1.911 12.56 0.0 0.0 0.0 372.0 0.0 56.0 293.8 0.072 9.0 0.0 47.0 47.0 47.0 1.75 503.0 0.0 12.0 0.075 198.0 138.0 160.0 0.457 30.7 142.0 0.0 0.040 160.0 0.170 0.89 0.0 0.0 2.0 1.05 0.00 0.3 76.15 1.29 0.004 0.000 0.004 0.006 0.000 0.033 2.231 0.811 0.201 3.411 0.027 0.000 1.555 0.048 0.000 0.188 0.000 0.007 0.058
36 115676 5910 1 10949.0 3 0.0 NaN 2 Pork, fresh, enhanced, loin, top loin (chops),... NaN 2.0 20.0 2006.0 0 50101 NaN 0.0 567.0000 510.299988 NaN NaN 1 NaN NaN 10.0 0 10.0 2.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.65 2.65 2.65 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.00 0.00 0.0 171.0 0.00 0.0 0.00 10.26 2.530 5.801 1.925 19.45 0.0 0.0 0.0 56.0 0.0 9.0 53.0 0.049 0.0 0.0 0.0 0.0 0.0 0.42 0.0 0.0 21.0 7.178 244.0 489.0 0.0 0.195 42.8 251.0 0.0 0.475 0.0 0.467 0.56 0.0 0.0 0.3 0.20 0.00 0.0 68.65 1.22 0.000 0.000 0.000 0.006 0.006 0.090 1.553 0.843 0.103 5.588 0.100 0.001 1.749 0.078 0.000 0.075 0.000 0.004 0.000
37 115676 5910 2 31101010.0 1 0.0 NaN 1 Egg, whole, raw NaN 2.0 25.0 2502.0 0 50601 NaN 0.0 600.0000 528.000000 NaN NaN 1 NaN 50.0 12.0 0 12.0 2.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 4.25 0.00 0.0 143.0 0.72 0.0 0.37 9.51 3.126 3.658 1.911 12.56 0.0 0.0 0.0 372.0 0.0 56.0 293.8 0.072 9.0 0.0 47.0 47.0 47.0 1.75 503.0 0.0 12.0 0.075 198.0 138.0 160.0 0.457 30.7 142.0 0.0 0.040 160.0 0.170 0.89 0.0 0.0 2.0 1.05 0.00 0.3 76.15 1.29 0.004 0.000 0.004 0.006 0.000 0.033 2.231 0.811 0.201 3.411 0.027 0.000 1.555 0.048 0.000 0.188 0.000 0.007 0.058
38 115676 5910 3 5100.0 3 0.0 NaN 2 Chicken, broilers or fryers, wing, meat and sk... NaN 2.0 22.0 2202.0 0 50202 NaN 0.0 1814.4000 979.776001 NaN NaN 1 NaN NaN 46.0 0 46.0 2.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.42 2.42 0.00 2.42 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 6.49 0.00 0.0 191.0 0.00 0.0 0.00 13.16 4.061 6.228 2.868 17.52 0.0 0.0 0.0 111.0 0.0 11.0 82.0 0.034 0.0 0.0 7.0 7.0 7.0 0.46 0.0 0.0 16.0 5.697 123.0 187.0 9.0 0.103 17.6 84.0 0.0 0.054 9.0 0.528 0.25 0.0 0.0 0.1 0.64 0.00 3.8 69.19 1.21 0.000 0.000 0.000 0.006 0.007 0.079 3.156 0.758 0.882 5.221 0.083 0.002 2.557 0.130 0.003 0.082 0.004 0.010 0.006
39 115676 5910 4 92410550.0 1 0.0 NaN 1 Soft drink, fruit flavored, caffeine containing Mellow Yellow; Mountain Dew; Big Red; 7-Up Gol... 7.0 72.0 7202.0 0 70304 NaN 0.0 4435.2000 4435.200195 NaN NaN 0 30.8 NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.00 2.14 0.0 37.0 9.56 0.0 8.97 0.02 0.000 0.000 0.000 0.07 0.0 0.0 0.0 0.0 8.0 2.0 0.3 0.001 0.0 0.0 0.0 0.0 0.0 0.11 0.0 0.0 0.0 0.000 10.0 2.0 0.0 0.000 0.1 4.0 0.0 0.000 0.0 0.000 0.00 0.0 0.0 0.0 0.00 0.00 0.0 90.31 0.02 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
40 115676 5910 5 11432000.0 1 0.0 NaN 1 Yogurt, fruit variety, lowfat milk Kissle; custard style; Dannon Lowfat Yogurt Fr... 1.0 18.0 1804.0 0 40203 NaN 0.0 680.4000 680.400024 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.33 0.00 0.00 0.33 0.04 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.47 2.87 0.0 102.0 19.05 0.0 19.05 1.08 0.697 0.297 0.031 4.37 0.0 0.0 2.0 4.0 0.0 152.0 14.0 0.080 0.0 0.0 9.0 9.0 9.0 0.07 0.0 0.0 15.0 0.095 119.0 195.0 10.0 0.178 3.1 58.0 0.0 0.037 10.0 0.040 0.47 0.0 0.7 0.6 0.02 0.00 0.1 74.48 0.74 0.032 0.022 0.014 0.031 0.037 0.114 0.294 0.105 0.024 0.247 0.000 0.000 0.022 0.009 0.000 0.000 0.000 0.000 0.000
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42 115676 5910 7 22959.0 3 0.0 NaN 1 Macaroni and cheese dinner with dry sauce mix,... NaN 3.0 32.0 3206.0 0 60401 NaN 0.0 340.2000 340.200012 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.28 0.28 0.00 0.00 0.00 0.00 0.00 0.00 2.47 2.47 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 4.62 0.00 0.0 379.0 70.12 3.2 9.61 4.82 1.151 0.665 0.813 13.86 0.0 0.0 5.0 7.0 0.0 146.0 36.0 0.193 0.0 187.0 334.0 17.0 204.0 2.75 15.0 0.0 49.0 3.882 331.0 347.0 17.0 0.626 47.5 680.0 0.0 1.040 18.0 0.142 0.77 0.0 0.7 0.0 0.13 0.00 0.2 8.05 1.23 0.000 0.000 0.000 0.000 0.000 0.164 0.770 0.217 0.000 0.665 0.000 0.000 0.813 0.000 0.000 0.000 0.000 0.000 0.000
43 115676 5910 8 75511010.0 1 0.0 NaN 1 Hot pepper sauce Tabasco sauce; Lousianna Hot Sauce; 8.0 84.0 8406.0 0 70201 NaN 0.0 340.2000 340.200012 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.25 0.00 0.25 0.0 0.25 0.00 0.0 0.00 0.00 0.00 0.00 0.00 0.0 12.0 0.80 0.6 0.13 0.76 0.106 0.061 0.401 1.29 0.0 62.0 919.0 0.0 0.0 12.0 0.0 0.075 68.0 1.0 2.0 0.0 1.0 1.16 10.0 0.0 12.0 0.178 23.0 128.0 0.0 0.084 0.5 633.0 0.0 0.032 82.0 0.154 0.00 0.0 4.5 0.0 0.01 0.00 0.2 95.17 0.16 0.000 0.000 0.000 0.000 0.000 0.002 0.090 0.014 0.002 0.058 0.000 0.000 0.398 0.003 0.000 0.000 0.000 0.000 0.000
44 115676 5910 9 57125000.0 1 0.0 NaN 2 Cinnamon Toast Crunch NaN 4.0 46.0 4602.0 0 10103 NaN 0.0 602.4375 602.437500 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.07 0.71 1.36 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 8.00 0.00 6.87 0.0 410.0 79.01 5.2 29.70 10.10 1.100 6.590 2.200 5.27 0.0 0.0 3.0 0.0 0.0 286.0 9.4 0.169 2.0 267.0 472.0 19.0 286.0 12.89 77.0 0.0 42.0 23.825 160.0 172.0 426.0 1.200 6.9 580.0 0.0 1.100 427.0 1.420 4.30 4.3 17.1 2.9 2.01 0.00 7.8 2.64 10.69 0.000 0.000 0.000 0.000 0.000 0.002 0.751 0.242 0.024 6.427 0.136 0.000 1.602 0.595 0.000 0.000 0.000 0.000 0.000
45 115676 5910 10 92301060.0 1 0.0 NaN 2 Tea, NS as to type, presweetened with sugar lemon- or other fruit-flavored; Arizona Iced T... 7.0 73.0 7304.0 0 70301 NaN 0.0 6002.8800 6002.879883 NaN NaN 0 29.6 NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.00 1.92 0.0 32.0 8.35 0.0 8.07 0.00 0.002 0.001 0.004 0.01 0.0 0.0 0.0 0.0 15.0 0.0 0.4 0.010 0.0 0.0 4.0 4.0 4.0 0.02 0.0 0.0 3.0 0.003 1.0 32.0 0.0 0.013 0.0 3.0 1.0 0.000 0.0 0.000 0.00 0.0 0.0 0.0 0.00 0.00 0.0 91.63 0.02 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.001 0.002 0.000 0.000 0.000 0.000 0.000
46 115676 5910 11 92307500.0 1 0.0 NaN 6 Half and Half beverage, half iced tea and half... NaN 7.0 73.0 7304.0 1 70304 92301130.0 0.0 3788.8000 3788.800049 NaN NaN 0 29.6 NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.00 2.26 0.0 39.0 10.04 0.0 9.64 0.02 0.003 0.001 0.007 0.02 0.0 0.0 0.0 0.0 9.0 2.0 0.5 0.011 1.0 0.0 3.0 3.0 3.0 0.02 1.0 0.0 2.0 0.012 1.0 23.0 0.0 0.009 0.1 4.0 1.0 0.002 0.0 0.003 0.00 0.0 1.5 0.0 0.01 0.00 0.0 89.86 0.02 0.000 0.000 0.000 0.000 0.000 0.000 0.002 0.000 0.000 0.001 0.000 0.000 0.004 0.003 0.000 0.000 0.000 0.000 0.000
47 115676 5910 12 94100100.0 1 0.0 NaN 2 Water, bottled, unsweetened plain; flavored; spring water, nonsparkling or... 7.0 77.0 7704.0 0 70306 NaN 0.0 24011.5200 24011.519531 NaN NaN 0 29.6 NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.00 0.0 0.00 0.00 0.000 0.000 0.000 0.00 0.0 0.0 0.0 0.0 0.0 10.0 0.0 0.007 0.0 0.0 0.0 0.0 0.0 0.00 0.0 0.0 2.0 0.000 0.0 0.0 0.0 0.000 0.0 2.0 0.0 0.000 0.0 0.000 0.00 0.0 0.0 0.0 0.00 0.00 0.0 99.98 0.00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
48 115676 5910 13 24198700.0 1 0.0 NaN 2 Chicken patty, fillet, or tenders, breaded, co... Weaver Chicken Breast Tenders; Tyson Breaded C... 2.0 22.0 2204.0 0 60201 NaN 0.0 1134.0000 1134.000000 NaN NaN 1 NaN NaN NaN 0 NaN NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.25 1.25 0.00 2.12 2.12 0.00 2.12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 11.50 0.00 0.00 0.0 250.0 18.47 2.5 1.24 13.81 2.390 4.124 6.150 13.03 0.0 0.0 9.0 26.0 0.0 48.0 46.2 0.170 7.0 7.0 33.0 20.0 28.0 1.11 44.0 0.0 41.0 4.161 205.0 349.0 4.0 0.067 16.7 483.0 0.0 0.154 6.0 0.250 0.18 0.0 0.1 0.1 2.75 0.00 5.6 52.52 0.76 0.000 0.000 0.004 0.005 0.003 0.022 1.871 0.387 0.198 3.852 0.059 0.002 5.897 0.196 0.001 0.024 0.000 0.003 0.002
49 115676 5910 14 23612.0 3 0.0 NaN 3 Beef, chuck, arm pot roast, separable lean onl... NaN 2.0 20.0 2002.0 0 50101 NaN 0.0 1020.6000 642.978027 NaN NaN 1 NaN NaN 37.0 0 37.0 2.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.65 2.65 2.65 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.00 0.00 0.0 0.00 0.00 0.00 0.00 0.00 0.0 139.0 0.00 0.0 0.00 5.05 1.875 2.119 0.232 21.96 0.0 0.0 0.0 65.0 0.0 15.0 91.7 0.091 0.0 0.0 13.0 13.0 13.0 2.02 0.0 0.0 23.0 5.221 201.0 332.0 0.0 0.168 24.5 74.0 0.0 0.077 0.0 0.489 2.11 0.0 0.0 0.1 0.29 0.00 1.2 72.51 5.48 0.000 0.000 0.000 0.000 0.004 0.133 1.121 0.616 0.184 1.931 0.004 0.000 0.176 0.017 0.000 0.026 0.002 0.011 0.001

Mensen met een lager inkomen doen minder vaak aan sport buiten werk#

df6 = pd.read_csv('diabetes_split.csv')
df6.head(5)
Diabetes_binary BMI Smoker PhysActivity Fruits Veggies AnyHealthcare NoDocbcCost GenHlth PhysHlth Sex Age Education Income
0 0.0 26.0 0.0 1.0 0.0 1.0 1.0 0.0 3.0 30.0 1.0 4.0 6.0 8.0
1 0.0 26.0 1.0 0.0 1.0 0.0 1.0 0.0 3.0 0.0 1.0 12.0 6.0 8.0
2 0.0 26.0 0.0 1.0 1.0 1.0 1.0 0.0 1.0 10.0 1.0 13.0 6.0 8.0
3 0.0 28.0 1.0 1.0 1.0 1.0 1.0 0.0 3.0 3.0 1.0 11.0 6.0 8.0
4 0.0 29.0 1.0 1.0 1.0 1.0 1.0 0.0 2.0 0.0 0.0 8.0 5.0 8.0 
df6['PhysActivity'] = df6['PhysActivity'].astype(int)

# Map the values to their respective labels
df6['PhysActivity'] = df6['PhysActivity'].map({0: 'No Physical Activity', 1: 'Physical Activity'})

# Fill NaN values in PhysActivity column with a placeholder (if any)
df6['PhysActivity'] = df6['PhysActivity'].fillna('Unknown')

# Filter DataFrame for PhysActivity == 'Physical Activity' (1) and PhysActivity == 'No Physical Activity' (0)
df_active = df6[df6['PhysActivity'] == 'Physical Activity']
df_inactive = df6[df6['PhysActivity'] == 'No Physical Activity']

# Calculate proportions within the DataFrame for each subset
df_active['Proportion'] = df_active.groupby(['Age', 'Income'])['PhysActivity'].transform('size') / len(df_active)
df_inactive['Proportion'] = df_inactive.groupby(['Age', 'Income'])['PhysActivity'].transform('size') / len(df_inactive)

# Create Bubble Plot for Physical Activity
fig1 = px.scatter(
    df_active,
    x='Age',
    y='Income',
    size='Proportion',
    color='PhysActivity',
    hover_name='PhysActivity',
    size_max=30,
    title='Bubble Plot: Income by Age and Physical Activity (Physical Activity)',
    labels={
        'Age': 'Age',
        'Income': 'Income',
        'Proportion': 'Proportion of Total Values',
        'PhysActivity': 'Physical Activity'
    },
    template='plotly_white'
)

fig1.update_layout(
    xaxis=dict(
        title='Age',
        tickvals=list(range(1, 14)),
        ticktext=list(range(1, 14))
    ),
    yaxis=dict(
        title='Income',
        tickvals=list(range(1, 9)),
        ticktext=list(range(1, 9))
    ),
    height=600
)

# Create Bubble Plot for No Physical Activity
fig2 = px.scatter(
    df_inactive,
    x='Age',
    y='Income',
    size='Proportion',
    color='PhysActivity',
    hover_name='PhysActivity',
    size_max=30,
    title='Bubble Plot: Income by Age and Physical Activity (No Physical Activity)',
    labels={
        'Age': 'Age',
        'Income': 'Income',
        'Proportion': 'Proportion of Total Values',
        'PhysActivity': 'Physical Activity'
    },
    template='plotly_white'
)

fig2.update_layout(
    xaxis=dict(
        title='Age',
        tickvals=list(range(1, 14)),
        ticktext=list(range(1, 14))
    ),
    yaxis=dict(
        title='Income',
        tickvals=list(range(1, 9)),
        ticktext=list(range(1, 9))
    ),
    height=600
)

# Show the figures
fig1.show()
fig2.show()

Reflection#

Van onze TA en medestudenten uit groep M kregen we feedback op onze draft tijdens een werkcollege 2 weken geleden. Wij presenteerden eerst ons werk, en kregen vervolgens feedback op de layout, visualisaties en argumenten. Hieronder staan de tips en feedback die wij hebben gekregen en ook gebruikt bij het schrijven van onze eindversie van de datastory:

  • De layout en het lettertype aanpassen om de datastory visueel prettig te maken.

  • Meer tekst schrijven bij de visualisaties.

Verder hebben we nagedacht over xxxxxx

References#

Contents